To examine the anthropometric characteristics of an Australian National Rugby League team and identify the relationship to type and incidence of injuries sustained during a professional season. It was hypothesised that body composition would not change discernibly across a season and that injury would be negatively related to preseason bone and muscle mass.A repeated measure, prospective, observational, cohort study.Griffith University, Gold Coast, Australia.37 professional male Australian National Rugby League players, 24.3 (3.8) years of age were recruited for preseason 1 testing, of whom 25 were retested preseason 2. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measures included biometrics; body composition (bone, muscle and fat mass; dual-energy x-ray absorptiometry; XR800, Norland Medical Systems, Inc); bone geometry and strength (peripheral quantitative CT; XCT 3000, Stratec); calcaneal broadband ultrasound attenuation (BUA; QUS-2, Quidel); diet and physical activity history. Secondary outcome measures included player injuries across a single playing season.Lean mass decreased progressively throughout the season (pre=81.45(7.76) kg; post=79.89(6.72) kg; p?0.05), while whole body (WB) bone mineral density (BMD) increased until mid-season (pre=1.235(0.087) g/cm(2); mid=1.296(0.093) g/cm(2); p?0.001) then decreased thereafter (post=1.256(0.100); p?0.001). Start-of-season WB BMD, fat and lean mass, weight and tibial mass measured at the 38% site predicted bone injury incidence, but no other relationship was observed between body composition and injury.Significant anthropometric changes were observed in players across a professional rugby league season, including an overall loss of muscle and an initial increase, followed by a decrease in bone mass. Strong relationships between anthropometry and incidence of injury were not observed. Long-term tracking of large rugby league cohorts is indicated to obtain more injury data in order to examine anthropometric relationships with greater statistical power.HubMed – rehab

INTRODUCTION: Exercise not only benefits physical and cardiovascular function in older adults with multiple chronic conditions, but may also improve cognitive function. Peak heart rate (HR), a physiological indicator for maximal effort, is the most common and practical means of establishing and monitoring exercise intensity. In particular, in the absence of graded maximal exercise tests (GXT) results, age-predicted maximal HR values are typically used. Using individuals with stroke as a model for examining older adults with co-existing cardiovascular and neuromotor conditions, the purpose of this paper was to examine the determinants associated with achieving age-predicted maximal HR on a GXT, with respect to neurological, cognitive and lower limb function. METHODS: Forty-seven participants with stroke (mean±SD age 67±7 years, 4±3 years post-stroke) performed GXTs. Peak values for gas exchange, HR and ratings of perceived exertion were noted. Logistic regression analysis was performed to examine determinants (neurological impairment, leg motor impairment, Montreal Cognitive Assessment (MoCA) score, walking ability) associated with the ability to achieve age-predicted maximal HR on the GXT. RESULTS: VO2peak was 16.5±6 ml•kg?min. Fourteen (30%) participants achieved ?100% of age-predicted maximal HR. Logistic regression modeling revealed that the ability to achieve this threshold was associated with less leg motor impairment (P=0.02, OR 2.3) and higher cognitive scores (P=0.048, OR 1.3). CONCLUSIONS: These results suggest that non-cardiopulmonary factors such as leg motor impairment and cognitive function are important contributors to achieving maximal effort during exercise tests. This study has important implications for post-stroke exercise prescription whereby training intensities that are based on peak HR from GXTs may be underestimated among individuals with cognitive and physical impairments.HubMed – rehab

ABSTRACT: Recent literature suggests that sub-concussive impacts may influence cognitive functioning across the lifespan. These effects are suggested to manifest as functional and possibly structural changes. Head impact biomechanics during American football have been characterized from the high school to professional level, but style of play has not been considered. PURPOSE: The aim of this investigation was to quantify and compare head impact frequencies and magnitudes between two different offensive schemes. METHODS: We investigated the frequencies and magnitudes (linear acceleration [g], rotational acceleration [rad/s], and HITsp) of head impacts sustained by 83 high school football athletes, playing forschools employing two different offensive schemes. The two schemes comprised of a run first offense (42 athletes) and a pass first offense (41 athletes). The Head Impact Telemetry System was used to record head impact measures. RESULTS: A total of 35,620 impacts were recordedacross two seasons. Athletes in the run first offense sustained an average of 456 head impacts per season (41 practices and 9 games) while the pass first offense athletes sustained an average of 304 head impacts per season (44 practices and 9 games).The pass first offense however sustained significantly higher impact magnitudes (p’s<0.05; 28.56g, 1777.58rad/s, and 16.24)than the run first offense (25.67g, 1675.36rad/s, and 15.48) across a season. CONCLUSION: These data provide a first look at how different offensive strategies may influencehead impact exposure in football athletes. In the study population, a run first offense was associated with more frequent head impacts, of smaller magnitude, than a pass first offense.
HubMed – rehab

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